Do AI Overviews Benefit Search Engines? An Ecosystem Perspective
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918314628874240 |
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| author | Wu, Yihang Tang, Jiajun Liu, Jinfei Xu, Haifeng Yao, Fan |
| author_facet | Wu, Yihang Tang, Jiajun Liu, Jinfei Xu, Haifeng Yao, Fan |
| contents | The integration of AI Overviews into search engines enhances user experience but diverts traffic from content creators, potentially discouraging high-quality content creation and causing user attrition that undermines long-term search engine profit. To address this issue, we propose a game-theoretic model of creator competition with costly effort, characterize equilibrium behavior, and design two incentive mechanisms: a citation mechanism that references sources within an AI Overview, and a compensation mechanism that offers monetary rewards to creators. For both cases, we provide structural insights and near-optimal profit-maximizing mechanisms. Evaluations on real click data show that although AI Overviews harm long-term search engine profit, interventions based on our proposed mechanisms can increase long-term profit across a range of realistic scenarios, pointing toward a more sustainable trajectory for AI-enhanced search ecosystems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_22493 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Do AI Overviews Benefit Search Engines? An Ecosystem Perspective Wu, Yihang Tang, Jiajun Liu, Jinfei Xu, Haifeng Yao, Fan Computer Science and Game Theory Information Retrieval The integration of AI Overviews into search engines enhances user experience but diverts traffic from content creators, potentially discouraging high-quality content creation and causing user attrition that undermines long-term search engine profit. To address this issue, we propose a game-theoretic model of creator competition with costly effort, characterize equilibrium behavior, and design two incentive mechanisms: a citation mechanism that references sources within an AI Overview, and a compensation mechanism that offers monetary rewards to creators. For both cases, we provide structural insights and near-optimal profit-maximizing mechanisms. Evaluations on real click data show that although AI Overviews harm long-term search engine profit, interventions based on our proposed mechanisms can increase long-term profit across a range of realistic scenarios, pointing toward a more sustainable trajectory for AI-enhanced search ecosystems. |
| title | Do AI Overviews Benefit Search Engines? An Ecosystem Perspective |
| topic | Computer Science and Game Theory Information Retrieval |
| url | https://arxiv.org/abs/2601.22493 |